An Explainable and Lightweight Improved 1-D CNN Model for Vibration Signals of Rotating Machinery

Previous 1-D convolutional neural network (1-D CNN) models for vibration fault diagnosis have high computational complexity and poor interpretability, which cannot meet the higher requirements of model storage, computational efficiency, and reliability for airborne and portable devices. Considering these challenges, an explainable and lightweight 1-D CNN (ELCNN) model based on square global average pooling (S-GAP) and improved vibration signals is proposed. The feature extraction and classification layers of 1-D CNN are optimized to minimize the model parameters and computational complexity and improve interpretability while ensuring diagnostic accuracy. The model compresses the number of convolutional layers, removes unnecessary bias, activation function, and pooling layer, and replaces the fully connected layer (FCL) with S-GAP. Improved 1-D CNN models of different methods are evaluated and analyzed on public datasets of rolling bearings. Results show that the ELCNN improved for vibration signals is more lightweight, antinoise, and explainable than other models, and the diagnostic accuracy is further improved.